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    A SILICON-BASED MICRO-MACHINED VIBRATORY GYROSCOPE WITH AN I-SHAPED STRUCTURE
    41.
    发明申请
    A SILICON-BASED MICRO-MACHINED VIBRATORY GYROSCOPE WITH AN I-SHAPED STRUCTURE 审中-公开

    公开(公告)号：US20190017822A1

    公开(公告)日：2019-01-17

    申请号：US16066634

    申请日：2016-09-26

    Applicant: NANJING UNIVERSITY OF SCIENCE AND TECHNOLOGY

    Inventor： Qin SHI ,  Anping QIU ,  Guoming XIA ,  Yang ZHAO

    IPC: G01C19/5621

    Abstract: The invention relates to a silicon-based micro-machined vibratory gyroscope with an I-shaped structure, which is a measuring instrument used for measuring the angular rate perpendicular to a base, and comprises a top monocrystalline silicon, a middle monocrystalline silicon, and a bottom monocrystalline silicon; the top monocrystalline silicon being arranged with signal input and output lines is the silicon micro gyroscope packaged cover plate; the middle monocrystalline silicon is where the gyroscope mechanical structures are fabricated; the bottom monocrystalline silicon is a gyroscope substrate with fixed pedestals; the middle monocrystalline silicon layer is sealed in a closed cavity formed by top and bottom monocrystalline silicon layers. The invented gyroscope has properties of small error, high mechanical sensitivity, low vibration sensitivity, and low temperature sensitivity, and can implement motion decoupling of a driving mode and a detection mode, large amplitude vibration, and detection output decoupling.

    Information traceability method and system based on blockchain
    46.
    发明授权
    Information traceability method and system based on blockchain 有权

    公开(公告)号：US11762879B2

    公开(公告)日：2023-09-19

    申请号：US18048824

    申请日：2022-10-21

    Applicant: NANJING UNIVERSITY OF SCIENCE AND TECHNOLOGY

    Inventor： Peng Zhu ,  Jun Wang

    IPC: G06F16/00 ,  G06F16/27 ,  G06F17/10

    CPC classification number: G06F16/27 ,  G06F17/10

    Abstract: The present invention provides an information traceability method and system based on a blockchain. The method includes: receiving a traceability request, where the traceability request includes a type label, a time label, and a content label; accessing information path data pre-stored in the blockchain based on the type label to obtain target path data, where the target path data is single-source path data or multi-source path data; determining a corresponding primary data set based on the time label, where the target path data includes a plurality of primary data sets; determining a corresponding secondary data set based on the content label, where the primary data set includes at least one secondary data set; and extracting path information from the secondary data set, and obtaining at least one information source based on the path information. In the technical solution of the present invention, an information source can be quickly located based on different labels in the traceability request during information traceability, which reduces a volume of data traversed during searching for the information source, saves a time for searching for the information source, and has relatively high efficiency.

    INTRUSION DETECTION METHOD AND SYSTEM FOR INTERNET OF VEHICLES BASED ON SPARK AND DEEP LEARNING
    47.
    发明申请
    INTRUSION DETECTION METHOD AND SYSTEM FOR INTERNET OF VEHICLES BASED ON SPARK AND DEEP LEARNING 有权

    公开(公告)号：US20220217170A1

    公开(公告)日：2022-07-07

    申请号：US17506607

    申请日：2021-10-20

    Applicant: Nanjing University of Science and Technology

    Inventor： Yong QI ,  Jianye YU

    IPC: H04L9/40 ,  G06N3/08

    Abstract: An intrusion detection method and system for Internet of Vehicles based on Spark and combined deep learning are provided. The method includes the following steps: S1: setting up Spark distributed cluster; S2: initializing the Spark distributed cluster, constructing a convolutional neural network (CNN) and long short-term memory (LSTM) combined deep learning algorithm model, initializing parameters, and uploading collected data to a Hadoop distributed file system (HDFS); S3: reading the data from the HDFS for processing, and inputting the data to the CNN-LSTM combined deep learning algorithm model, for recognizing the data; and S4: dividing the data into multiple resilient distributed datasets (RDDs) for batch training with a preset number of iterations.

    A QUANTITATIVE PHASE IMAGING METHOD BASED ON DIFFERENTIAL PHASE CONTRAST WITH OPTIMAL LIGHTING PATTERN DESIGN
    48.
    发明申请
    A QUANTITATIVE PHASE IMAGING METHOD BASED ON DIFFERENTIAL PHASE CONTRAST WITH OPTIMAL LIGHTING PATTERN DESIGN 有权

    公开(公告)号：US20210325654A1

    公开(公告)日：2021-10-21

    申请号：US17294322

    申请日：2019-07-05

    Applicant: Nanjing University of Science and Technology

    Inventor： Qian CHEN ,  Chao ZUO ,  Yao FAN ,  Jiasong SUN ,  Jiaji LI ,  Shijie FENG ,  Yuzhen ZHANG

    IPC: G02B21/14 ,  G02B21/36 ,  H04N5/235 ,  H04N5/232

    Abstract: The patent discloses a differential phase contrast (DPC) quantitative phase microscopy method based on the optimal illumination pattern design. Firstly, the optimal illumination pattern corresponding to the isotropic phase transfer function of DPC quantitative phase imaging is derived, which is determined as a semi-annular illumination pattern with the illumination numerical aperture NAill equal to the numerical aperture NAobj of the objective lens. The illumination intensity distribution varies with the cosine of the illumination angle, and it can be expressed as S(θ)=cos(θ). This patent effectively compensates for the frequency loss of phase transfer, not only the high-frequency responses of PTF are enhanced, but also the transfer responses of low-frequency phase information is significantly improved. As a result, the optimal illumination scheme ensures the correctness and achieves high resolution phase reconstruction, while the number of illuminations is reduced to a minimum of two, which greatly increases the imaging speed, allowing for real-time dynamic, high-correctness, high-resolution phase imaging results.

    PROGRAMMABLE ANNULAR LED ILLUMINATION-BASED HIGH EFFICIENCY QUANTITATIVE PHASE MICROSCOPY IMAGING METHOD
    49.
    发明申请
    PROGRAMMABLE ANNULAR LED ILLUMINATION-BASED HIGH EFFICIENCY QUANTITATIVE PHASE MICROSCOPY IMAGING METHOD 审中-公开

    公开(公告)号：US20200209604A1

    公开(公告)日：2020-07-02

    申请号：US16633037

    申请日：2018-02-26

    Applicant: NANJING UNIVERSITY OF SCIENCE AND TECHNOLOGY

    Inventor： Qian CHEN ,  Chao Zuo ,  Jiasong SUN ,  Shijie FENG ,  Yuzhen ZHANG ,  Guohua GU

    IPC: G02B21/06 ,  G02B21/36

    Abstract: The invention discloses a programmable annular LED illumination-based high efficiency quantitative phase microscopy imaging method, the proposed method comprising the following steps: the derivation of system optical transfer function in a partially coherent illumination imaging system; the derivation of phase transfer function with the weak object approximation under the illumination of tilted axially symmetric coherent point illumination source; the extension of illumination from an axially symmetric coherence point source to a discrete annular point source, and the optical transfer function can be treated as an incoherent superposition of each pair of tilted axially symmetric coherent point sources. The acquisition of raw intensity dataset; the implementation of deconvolution for quantitative phase reconstruction. The invention derives the system phase transfer function under the tilted axially symmetric point light source in the case of partially coherent illumination, and promotes the optical phase transfer function of the discrete annular point light source. The programmability characteristic of LED array enables the annular illumination aperture to be flexibly adjustable, being applicable to different microscopic objects with different numerical apertures, and improving the compatibility and flexibility of the system.

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